R Notebook

library(nycflights13)
library(tidyverse)

Registered S3 methods overwritten by 'ggplot2':
  method         from 
  [.quosures     rlang
  c.quosures     rlang
  print.quosures rlang
── Attaching packages ─────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.3     ✔ stringr 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0
── Conflicts ────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

install.packages("nycflights13")

Error in install.packages : Updating loaded packages

"nycflights13"

[1] "nycflights13"

5.2.4 Exercises

1

?flights
flights

filter(flights, arr_delay >= 2) #1

filter(flights, dest == "IAH") #2

airlines

filter(flights, (carrier == "UA" | carrier == "AA" | carrier == "DL")) #3

filter(flights, (month == 7 | month == 8 | month == 9)) #4

filter(flights, arr_delay > 2, dep_delay == 0) #5

filter(flights, dep_delay <= 1, air_time >= 30) #6

filter(flights, (dep_time >= 0000 | dep_time <= 600)) #7

2

Restarting R session...

It finds the values between two values. This code could have been useful for number 7.

3

dep<-select(flights, dep.time)

is.na.data.frame(flights)

           year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay
     [1,] FALSE FALSE FALSE    FALSE          FALSE     FALSE    FALSE          FALSE     FALSE
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          carrier flight tailnum origin  dest air_time distance  hour minute time_hour
     [1,]   FALSE  FALSE   FALSE  FALSE FALSE    FALSE    FALSE FALSE  FALSE     FALSE
     [2,]   FALSE  FALSE   FALSE  FALSE FALSE    FALSE    FALSE FALSE  FALSE     FALSE
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 [ reached getOption("max.print") -- omitted 336724 rows ]

4

These aren’t missing because those would have to be exactly present in the data.

5.3.1 Exercises

1

arrange(flights)

arrange(flights, desc(dep_delay)) #2

arrange(flights, desc(air_time)) #3

4

Flight 15 travelled the longest while flight 167 traveled the shortest.

5.4.1 Exercises

2

select(flights, dep.time, dep_delay, arr_time, arr_delay)

This code only shows the values from the columns that I put in the code.

3

4

This doesn’t surprise me because every column shown has “time” in the title just like we had asked for in the code.

5.5.2 Exercises

1.

transmute(flights,
       sched_arr_time - dep_time)

2.

select(flights, air_time)

transmute(flights, 
            arr_time - dep_time)

The calculation has larger numbers.

3

select(flights, dep_time, sched_dep_time, dep_delay)

dep_time should be the same number as sched_dep_time and dep_delay should be zero.

4

?min_rank

5

1:3 + 1:10

longer object length is not a multiple of shorter object length

 [1]  2  4  6  5  7  9  8 10 12 11

Results in an error because these ratios cannot be used as values to use for calculations.

6

R provides tangent, cosine, sin, and their inverses.

5.6.7 Exercises

1

You can use group_by(), summarise(), count(), sum(), and ungroup() Departure delay is more important.

2

3

Departure delay is more important.

6

?sort

It sorts data frames.

5.7.1 Exercises

1

When mutate is combined to grouping, the new column made is grouped and ordered by the variables given in the code.

2

arrange(flights, desc(tailnum))

3

arrange(flights, min_rank(dep_delay))

You should leave around 8pm.

4

flights %>%
  group_by(dest) %>%
  select(dest)

5

?lag